code: def _create_model(model_kwargs, variant, pretrained=False):
      as_sequential = model_kwargs.pop("as_sequential", False)
      model = MobileNetV3(**model_kwargs)
      if pretrained and model_urls[variant]:
          load_pretrained(model, model_urls[variant])
      if as_sequential:
          model = model.as_sequential()
      return model
  
imports: import torch.nn
  import torch.nn.functional
  from .activations import get_act_fn, get_act_layer, HardSwish
  from .config import layer_config_kwargs
  from .conv2d_layers import select_conv2d
  from .helpers import load_pretrained
  from .efficientnet_builder import *
label_desc: Use image node_id 1 for calling a global function with example usage: _create_model(model_kwargs, variant, pretrained) and returns: model